lets_plot.geom_density2df¶
-
lets_plot.geom_density2df(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, kernel=None, adjust=None, bw=None, n=None, bins=None, binwidth=None, **other_args)¶ Fill density function contour.
- Parameters
mapping (FeatureSpec) – Set of aesthetic mappings created by aes() function. Aesthetic mappings describe the way that variables in the data are mapped to plot “aesthetics”.
data (dict or DataFrame) – The data to be displayed in this layer. If None, the default, the data is inherited from the plot data as specified in the call to ggplot.
stat (str, default=’density2df’) – The statistical transformation to use on the data for this layer, as a string.
position (str or FeatureSpec) – Position adjustment, either as a string (‘identity’, ‘stack’, ‘dodge’, …), or the result of a call to a position adjustment function.
show_legend (bool, default=True) – False - do not show legend for this layer.
sampling (FeatureSpec) – Result of the call to the sampling_xxx() function. Value None (or ‘none’) will disable sampling for this layer.
tooltips (layer_tooltips) – Result of the call to the layer_tooltips() function. Specifies appearance, style and content.
kernel (str, default=’gaussian’) – The kernel we use to calculate the density function. Choose among ‘gaussian’, ‘cosine’, ‘optcosine’, ‘rectangular’ (or ‘uniform’), ‘triangular’, ‘biweight’ (or ‘quartic’), ‘epanechikov’ (or ‘parabolic’).
bw (str or list of float) – The method (or exact value) of bandwidth. Either a string (choose among ‘nrd0’ and ‘nrd’), or a float array of length 2.
adjust (float) – Adjust the value of bandwidth my multiplying it. Changes how smooth the frequency curve is.
n (list of int) – The number of sampled points for plotting the function (on x and y direction correspondingly).
bins (int) – Number of levels.
binwidth (float) – Distance between levels.
other_args – Other arguments passed on to the layer. These are often aesthetics settings used to set an aesthetic to a fixed value, like color=’red’, fill=’blue’, size=3 or shape=21. They may also be parameters to the paired geom/stat.
- Returns
Geom object specification.
- Return type
LayerSpec
Note
geom_density2df() fills density contours.
- geom_density2df() understands the following aesthetics mappings:
x : x-axis coordinates.
alpha : transparency level of a layer. Understands numbers between 0 and 1.
fill : color of geometry filling.
Examples
>>> import numpy as np >>> from lets_plot import * >>> LetsPlot.setup_html() >>> n = 1000 >>> np.random.seed(42) >>> x = np.random.normal(size=n) >>> y = np.random.normal(size=n) >>> ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \ >>> geom_density2df()
>>> import numpy as np >>> from lets_plot import * >>> LetsPlot.setup_html() >>> n = 1000 >>> np.random.seed(42) >>> x = np.random.normal(size=n) >>> y = np.random.normal(size=n) >>> ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \ >>> geom_density2df(aes(fill='..group..'), show_legend=False) + \ >>> scale_fill_brewer(type='seq', palette='GnBu', direction=-1)
>>> import numpy as np >>> from lets_plot import * >>> LetsPlot.setup_html() >>> n = 1000 >>> np.random.seed(42) >>> x = np.random.normal(size=n) >>> y = np.random.normal(size=n) >>> p = ggplot({'x': x, 'y': y}, aes(x='x', y='y')) >>> bunch = GGBunch() >>> for i, bw in enumerate([.2, .4]): >>> for j, n in enumerate([16, 256]): >>> bunch.add_plot(p + geom_density2df(kernel='epanechikov', bw=bw, n=n, \ >>> size=.5, color='white') + \ >>> ggtitle('bw={0}, n={1}'.format(bw, n)), >>> j * 400, i * 400, 400, 400) >>> bunch.show()
>>> import numpy as np >>> from lets_plot import * >>> LetsPlot.setup_html() >>> n = 1000 >>> np.random.seed(42) >>> x = np.random.normal(size=n) >>> y = np.random.normal(size=n) >>> p = ggplot({'x': x, 'y': y}, aes(x='x', y='y')) >>> bunch = GGBunch() >>> for i, adjust in enumerate([1.5, 2.5]): >>> for j, bins in enumerate([5, 15]): >>> bunch.add_plot(p + geom_density2df(kernel='cosine', \ >>> size=.5, color='white', \ >>> adjust=adjust, bins=bins) + \ >>> ggtitle('adjust={0}, bins={1}'.format(adjust, bins)), >>> j * 400, i * 400, 400, 400) >>> bunch.show()